
pip install plotly

Requirement already satisfied: plotly in c:\users\sneha\anaconda3\lib\site-packages (5.7.0)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\sneha\anaconda3\lib\site-packages (from plotly) (8.0.1)
Requirement already satisfied: six in c:\users\sneha\anaconda3\lib\site-packages (from plotly) (1.15.0)
Note: you may need to restart the kernel to use updated packages.


import numpy as np
import pandas as pd
from pandas import DataFrame
import plotly.express as px
import seaborn as sns
%matplotlib inline
import matplotlib.pyplot as plt
import ipywidgets

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression


df=pd.read_csv('https://raw.githubusercontent.com/SnehaBaranwal/snehab2412.github.io/master/loans_full_schema.csv') #reading the csv file
df


df.dtypes

emp_title                            object
emp_length                          float64
state                                object
homeownership                        object
annual_income                       float64
verified_income                      object
debt_to_income                      float64
annual_income_joint                 float64
verification_income_joint            object
debt_to_income_joint                float64
delinq_2y                             int64
months_since_last_delinq            float64
earliest_credit_line                  int64
inquiries_last_12m                    int64
total_credit_lines                    int64
open_credit_lines                     int64
total_credit_limit                    int64
total_credit_utilized                 int64
num_collections_last_12m              int64
num_historical_failed_to_pay          int64
months_since_90d_late               float64
current_accounts_delinq               int64
total_collection_amount_ever          int64
current_installment_accounts          int64
accounts_opened_24m                   int64
months_since_last_credit_inquiry    float64
num_satisfactory_accounts             int64
num_accounts_120d_past_due          float64
num_accounts_30d_past_due             int64
num_active_debit_accounts             int64
total_debit_limit                     int64
num_total_cc_accounts                 int64
num_open_cc_accounts                  int64
num_cc_carrying_balance               int64
num_mort_accounts                     int64
account_never_delinq_percent        float64
tax_liens                             int64
public_record_bankrupt                int64
loan_purpose                         object
application_type                     object
loan_amount                           int64
term                                  int64
interest_rate                       float64
installment                         float64
grade                                object
sub_grade                            object
issue_month                          object
loan_status                          object
initial_listing_status               object
disbursement_method                  object
balance                             float64
paid_total                          float64
paid_principal                      float64
paid_interest                       float64
paid_late_fees                      float64
dtype: object


plt.figure(figsize=(15, 10))

plt.subplot(2, 2, 1)
grade = sorted(df.grade.unique().tolist())
sns.countplot(x='grade', data=df, hue='initial_listing_status', order=grade)

plt.subplot(2, 2, 2)
sub_grade = sorted(df.sub_grade.unique().tolist())
g = sns.countplot(x='sub_grade', data=df, hue='initial_listing_status', order=sub_grade)
g.set_xticklabels(g.get_xticklabels(), rotation=90);


ls= df.groupby('loan_status').count()

ls


ls_title= df['loan_status'].unique()


ls_title

array(['Current', 'Fully Paid', 'In Grace Period', 'Late (31-120 days)',
       'Charged Off', 'Late (16-30 days)'], dtype=object)


import plotly.express as px
import numpy
  
s = df['loan_status'].value_counts() ## Counts the occurrence of unqiue elements and stores in a variable called "s" which is series type
new = pd.DataFrame({'loan_status':s.index, 'Count':s.values})


s.values

array([9375,  447,   67,   66,   38,    7], dtype=int64)


import plotly.express as px
import numpy
  
fig = px.pie(values=s.values, names=s.index, title='Percenatage of loan_status per category',color=s.index,color_discrete_sequence=px.colors.qualitative.G10)
fig.show()


states= df['state'].unique()


states

array(['NJ', 'HI', 'WI', 'PA', 'CA', 'KY', 'MI', 'AZ', 'NV', 'IL', 'FL',
       'SC', 'CO', 'TN', 'TX', 'VA', 'NY', 'GA', 'MO', 'AR', 'MD', 'NC',
       'NE', 'WV', 'NH', 'UT', 'DE', 'MA', 'OR', 'OH', 'OK', 'SD', 'MN',
       'AL', 'WY', 'LA', 'IN', 'KS', 'MS', 'WA', 'ME', 'VT', 'CT', 'NM',
       'AK', 'MT', 'RI', 'ND', 'DC', 'ID'], dtype=object)


amount=df.groupby('state')['loan_amount'].sum()


amount

state
AK      632850
AL     1946550
AR      979775
AZ     4113475
CA    22073700
CO     4020525
CT     3258925
DC      394500
DE      300600
FL    11203675
GA     5545300
HI      674500
ID      636950
IL     6776600
IN     2692325
KS     1405500
KY     1509475
LA     1631175
MA     3801875
MD     3902375
ME      347275
MI     4036700
MN     2769525
MO     2651825
MS     1106850
MT      386200
NC     4809225
ND      258500
NE      758825
NH      722225
NJ     5665850
NM      695075
NV     2638750
NY    12589650
OH     5434125
OK     1448350
OR     2129450
PA     4665825
RI      751375
SC     2613900
SD      308700
TN     2651000
TX    13755275
UT      938700
VA     4465225
VT      336775
WA     3860700
WI     2028875
WV      981650
WY      312175
Name: loan_amount, dtype: int64


import pandas as pd
import plotly.express as px
import statsmodels.formula.api as smapi
import numpy as np

fig = px.bar(df, states, amount, color=df['state'].unique(),
                 labels={
                     "states": "State",
                     "amount": "Loan Amount",
                                     },
                title="Loan Amount for every State"
                )
fig.show()


plt.figure(figsize=(15, 20))

plt.subplot(4, 2, 1)
sns.countplot(x='term', data=df, hue='application_type', palette='RdPu', edgecolor='black')

plt.subplot(4, 2, 2)
sns.countplot(x='homeownership', data=df, hue='application_type', palette='RdPu', edgecolor='black')

plt.subplot(4, 2, 3)
sns.countplot(x='verified_income', data=df, hue='application_type', palette='RdPu',edgecolor='black')

plt.subplot(4, 2, 4)
g = sns.countplot(x='loan_purpose', data=df, hue='application_type', palette='RdPu',edgecolor='black')
g.set_xticklabels(g.get_xticklabels(), rotation=90);


paidtotal=df.groupby('loan_purpose')['paid_total'].sum()


paidtotal

loan_purpose
car                   1.880338e+05
credit_card           4.999259e+06
debt_consolidation    1.372605e+07
home_improvement      1.766449e+06
house                 4.151339e+05
major_purchase        8.362259e+05
medical               4.216902e+05
moving                1.275343e+05
other                 1.998309e+06
renewable_energy      1.468471e+04
small_business        3.676864e+05
vacation              8.128827e+04
Name: paid_total, dtype: float64


paidprincipal=df.groupby('loan_purpose')['paid_principal'].sum()


paidprincipal

loan_purpose
car                     136720.19
credit_card            3791547.82
debt_consolidation    10254801.85
home_improvement       1370570.81
house                   330449.70
major_purchase          673944.02
medical                 362076.41
moving                  103924.02
other                  1572277.12
renewable_energy          9332.35
small_business          271651.94
vacation                 67188.43
Name: paid_principal, dtype: float64


paidint=df.groupby('loan_purpose')['paid_interest'].sum()


paidint

loan_purpose
car                     51313.63
credit_card           1207487.60
debt_consolidation    3470732.04
home_improvement       395801.79
house                   84631.22
major_purchase         162251.86
medical                 59553.64
moving                  23610.29
other                  425829.13
renewable_energy         5352.36
small_business          96004.41
vacation                14099.84
Name: paid_interest, dtype: float64


paidlate=df.groupby('loan_purpose')['paid_late_fees'].sum()


paidlate

loan_purpose
car                     0.00
credit_card           223.73
debt_consolidation    518.93
home_improvement       76.62
house                  52.98
major_purchase         30.00
medical                60.12
moving                  0.00
other                 202.78
renewable_energy        0.00
small_business         30.00
vacation                0.00
Name: paid_late_fees, dtype: float64


import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(rows=2, cols=2, start_cell="bottom-left",subplot_titles=('Paid Interest',
                                    'Paid Late','Paid Total','Paid Principal'))

fig.add_trace(go.Scatter(y=df['loan_purpose'].unique(), x=paidtotal, mode='markers'),
              row=1, col=1)

fig.add_trace(go.Scatter(y=df['loan_purpose'].unique(), x=paidprincipal, mode='markers'),
              row=1, col=2)

fig.add_trace(go.Scatter(y=df['loan_purpose'].unique(), x=paidint,mode='markers'),
              row=2, col=1)

fig.add_trace(go.Scatter(y=df['loan_purpose'].unique(), x=paidlate,mode='markers'),
              row=2, col=2)

fig.show()


for col in df.columns:
    print(col)

emp_title
emp_length
state
homeownership
annual_income
verified_income
debt_to_income
annual_income_joint
verification_income_joint
debt_to_income_joint
delinq_2y
months_since_last_delinq
earliest_credit_line
inquiries_last_12m
total_credit_lines
open_credit_lines
total_credit_limit
total_credit_utilized
num_collections_last_12m
num_historical_failed_to_pay
months_since_90d_late
current_accounts_delinq
total_collection_amount_ever
current_installment_accounts
accounts_opened_24m
months_since_last_credit_inquiry
num_satisfactory_accounts
num_accounts_120d_past_due
num_accounts_30d_past_due
num_active_debit_accounts
total_debit_limit
num_total_cc_accounts
num_open_cc_accounts
num_cc_carrying_balance
num_mort_accounts
account_never_delinq_percent
tax_liens
public_record_bankrupt
loan_purpose
application_type
loan_amount
term
interest_rate
installment
grade
sub_grade
issue_month
loan_status
initial_listing_status
disbursement_method
balance
paid_total
paid_principal
paid_interest
paid_late_fees


#Create input and output features for the models
output_features =  df[["interest_rate"]]
print(output_features)

      interest_rate
0             14.07
1             12.61
2             17.09
3              6.72
4             14.07
...             ...
9995           7.35
9996          19.03
9997          23.88
9998           5.32
9999          10.91

[10000 rows x 1 columns]


#Selection of input features that can effect the interest rate
input_features = df[["loan_amount","term","grade","loan_purpose"]]
print(input_features)

      loan_amount  term grade        loan_purpose
0           28000    60     C              moving
1            5000    36     C  debt_consolidation
2            2000    36     D               other
3           21600    36     A  debt_consolidation
4           23000    36     C         credit_card
...           ...   ...   ...                 ...
9995        24000    36     A               other
9996        10000    36     D  debt_consolidation
9997        30000    36     E  debt_consolidation
9998        24000    36     A               other
9999        12800    36     B         credit_card

[10000 rows x 4 columns]


#Identified categorical values
input_features["loan_purpose"].unique()

array(['moving', 'debt_consolidation', 'other', 'credit_card',
       'home_improvement', 'medical', 'house', 'small_business', 'car',
       'major_purchase', 'vacation', 'renewable_energy'], dtype=object)


input_features["grade"].unique()

array(['C', 'D', 'A', 'B', 'F', 'E', 'G'], dtype=object)


output_features["interest_rate"].unique()

array([14.07, 12.61, 17.09,  6.72, 13.59, 11.99,  6.71, 15.04,  9.92,
        9.43, 19.03, 28.72, 26.77, 15.05,  6.08, 11.98,  7.96,  7.34,
        5.32,  6.07, 12.62,  9.44, 20.39,  9.93, 21.45, 10.42, 18.06,
       22.91, 30.79, 17.47,  5.31,  7.97, 14.08, 19.42, 10.91, 16.02,
       13.58, 16.01, 20.  , 21.85, 10.9 , 23.87,  7.35, 23.88, 25.82,
       10.41, 18.45, 30.17, 24.85, 25.81, 24.84, 30.75, 29.69, 26.3 ,
       22.9 ,  6.  , 30.65, 30.94])


#Applied get_dummies() method and then dropped the old columns
dummies = pd.get_dummies(input_features.grade)

merged = pd.concat([input_features, dummies], axis='columns')
merged


dummies2 = pd.get_dummies(merged.loan_purpose)

merged2 = pd.concat([merged, dummies2], axis='columns')
merged2


input_features_final = merged2.drop(['loan_purpose', 'grade'], axis='columns')


input_features_final


input_features_final.isnull().values.any()

False


# #Convert the output features from floats to ints as the classifier expects categorical data

# output_features['interest_rate'] = output_features['interest_rate'].astype(int)


#Split the dataset into train and test inputs for the model
X_train, X_test, y_train, y_test = train_test_split(input_features_final,output_features,test_size = 0.3)


#Checking the consistency of the data sets
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

(7000, 21)
(3000, 21)
(7000, 1)
(3000, 1)


model_y_train = y_train["interest_rate"].astype(int)
model_y_test = y_test["interest_rate"].astype(int)


X_train


y_train


model_y_train

3157     9
8260     6
4717     6
1087    26
6293    12
        ..
2881    15
449      7
6118    16
5550     9
5463     6
Name: interest_rate, Length: 7000, dtype: int32


#Import the model (Random Forest Classifier)
model = RandomForestClassifier(bootstrap=False, max_depth=90, max_features='sqrt',
                       min_samples_split=5, n_estimators=800, random_state=0)


model.fit(X_train, model_y_train)

RandomForestClassifier(bootstrap=False, max_depth=90, max_features='sqrt',
                       min_samples_split=5, n_estimators=800, random_state=0)


predict = model.predict(X_test)


predict

array([13, 21,  6, ...,  5, 12,  5])


answer = np.round(predict, 2)
print(answer)

[13 21  6 ...  5 12  5]


mean_squared_error(model_y_test,answer) # We can see the mean squared error is bit high

2.472


y_test= y_test.to_numpy()
y_test = [item for sublist in y_test for item in sublist]


plt.figure(figsize=(10,10))
plt.scatter(y_test, answer, c='crimson')

p1 = max(max(answer), max(y_test))
p2 = min(min(answer), min(y_test))
plt.plot([p1, p2], [p1, p2], 'b-')
plt.xlabel('True Values', fontsize=15)
plt.ylabel('Predictions', fontsize=15)
plt.axis('equal')
plt.show()


#Linear Regression
model2 = LinearRegression()


model2.fit(X_train, model_y_train)

LinearRegression()


max(y_test)

30.79


predict = model2.predict(X_test)
print(predict)

[13.84319321 18.67746091  6.3426567  ...  6.19649795 13.94977843
  6.18251406]


answer = np.round(predict, 2)
print(answer)

[13.84 18.68  6.34 ...  6.2  13.95  6.18]


y_test

[13.59,
 17.47,
 7.97,
 7.97,
 13.59,
 28.72,
 11.99,
 7.35,
 12.62,
 18.45,
 9.93,
 13.59,
 21.45,
 10.41,
 12.61,
 21.85,
 9.43,
 13.59,
 13.58,
 12.61,
 21.45,
 10.41,
 6.08,
 13.59,
 10.91,
 18.45,
 6.72,
 9.44,
 7.35,
 10.91,
 16.01,
 20.0,
 19.42,
 10.42,
 7.97,
 13.59,
 11.99,
 5.32,
 9.44,
 13.59,
 7.97,
 19.03,
 15.04,
 7.97,
 9.92,
 15.04,
 13.58,
 11.99,
 10.41,
 14.08,
 21.45,
 9.93,
 15.05,
 16.01,
 12.62,
 24.85,
 6.72,
 9.43,
 9.92,
 7.97,
 10.41,
 10.41,
 18.06,
 13.58,
 9.44,
 9.92,
 7.96,
 10.9,
 17.09,
 5.31,
 15.04,
 7.35,
 9.44,
 6.07,
 7.35,
 12.62,
 9.93,
 13.58,
 12.62,
 6.08,
 12.61,
 9.93,
 6.72,
 7.34,
 6.72,
 7.97,
 6.72,
 7.96,
 18.06,
 20.0,
 20.39,
 29.69,
 10.42,
 14.07,
 24.85,
 9.44,
 18.45,
 17.09,
 18.45,
 14.07,
 17.09,
 5.32,
 25.82,
 12.62,
 22.91,
 17.09,
 9.92,
 13.59,
 7.96,
 10.42,
 15.05,
 5.31,
 6.08,
 14.07,
 14.07,
 12.61,
 15.04,
 15.04,
 14.08,
 9.92,
 20.0,
 19.03,
 24.84,
 24.85,
 10.91,
 5.32,
 9.43,
 10.42,
 9.92,
 13.59,
 7.34,
 11.99,
 10.42,
 7.34,
 13.59,
 14.08,
 5.32,
 6.08,
 26.77,
 12.62,
 14.08,
 23.88,
 7.35,
 16.02,
 7.96,
 17.09,
 17.47,
 11.99,
 7.97,
 17.09,
 11.99,
 9.93,
 13.59,
 10.91,
 12.62,
 7.34,
 9.92,
 7.96,
 7.34,
 7.96,
 19.42,
 11.99,
 7.34,
 7.34,
 18.06,
 17.09,
 9.93,
 18.06,
 20.39,
 7.97,
 11.98,
 7.96,
 5.32,
 11.98,
 14.08,
 6.07,
 9.44,
 10.42,
 5.32,
 13.58,
 7.34,
 10.41,
 9.43,
 7.35,
 16.02,
 5.32,
 6.07,
 7.34,
 5.32,
 6.08,
 16.02,
 6.08,
 25.82,
 12.62,
 14.08,
 7.35,
 14.07,
 11.99,
 25.81,
 17.09,
 20.39,
 6.72,
 13.59,
 13.59,
 11.98,
 11.99,
 24.85,
 10.42,
 12.62,
 9.44,
 5.32,
 16.02,
 5.31,
 5.31,
 9.92,
 15.04,
 12.62,
 15.05,
 10.91,
 11.99,
 15.04,
 10.42,
 6.07,
 19.42,
 11.98,
 13.58,
 10.42,
 11.99,
 19.42,
 21.85,
 10.42,
 10.9,
 16.02,
 9.44,
 9.92,
 11.99,
 20.0,
 24.84,
 24.85,
 9.93,
 9.44,
 10.91,
 6.08,
 9.92,
 17.09,
 13.59,
 26.3,
 16.02,
 15.04,
 5.32,
 15.05,
 9.92,
 10.41,
 7.35,
 13.58,
 20.0,
 24.84,
 7.97,
 10.42,
 19.03,
 6.08,
 15.04,
 6.08,
 5.31,
 5.32,
 7.96,
 7.35,
 11.98,
 9.43,
 14.08,
 9.92,
 10.42,
 5.32,
 14.08,
 26.77,
 9.44,
 15.05,
 12.61,
 7.97,
 19.42,
 16.01,
 13.58,
 11.98,
 7.34,
 6.08,
 7.96,
 11.98,
 18.45,
 10.42,
 16.01,
 9.93,
 5.31,
 21.45,
 9.44,
 7.96,
 16.02,
 13.58,
 10.9,
 10.9,
 13.58,
 7.34,
 12.62,
 6.08,
 5.31,
 7.97,
 12.61,
 12.61,
 10.9,
 7.96,
 7.35,
 10.9,
 9.93,
 21.45,
 15.05,
 9.93,
 6.08,
 9.44,
 20.0,
 12.61,
 6.72,
 11.98,
 9.92,
 19.42,
 15.04,
 5.31,
 20.39,
 10.91,
 16.01,
 15.05,
 6.71,
 14.08,
 6.08,
 7.97,
 5.32,
 20.39,
 6.72,
 6.08,
 16.01,
 6.08,
 11.99,
 16.02,
 12.61,
 19.03,
 18.45,
 30.17,
 7.35,
 9.93,
 5.32,
 12.61,
 10.41,
 6.07,
 9.43,
 9.43,
 20.0,
 11.99,
 22.9,
 7.97,
 10.42,
 9.93,
 20.39,
 13.59,
 7.35,
 7.97,
 14.07,
 21.85,
 11.98,
 10.42,
 17.47,
 7.35,
 6.72,
 9.92,
 7.97,
 6.72,
 18.06,
 7.96,
 9.43,
 16.02,
 5.32,
 11.98,
 9.43,
 12.61,
 15.04,
 18.45,
 14.08,
 7.35,
 12.62,
 13.58,
 9.93,
 11.98,
 10.91,
 6.72,
 26.3,
 11.98,
 6.72,
 6.72,
 11.99,
 9.93,
 17.09,
 12.61,
 7.97,
 20.0,
 23.88,
 6.08,
 6.71,
 21.85,
 26.77,
 9.43,
 17.09,
 18.45,
 12.61,
 15.05,
 7.35,
 10.91,
 12.61,
 7.97,
 13.59,
 13.59,
 10.9,
 12.61,
 7.35,
 12.61,
 19.03,
 6.71,
 19.03,
 18.06,
 24.85,
 10.9,
 14.07,
 9.44,
 15.04,
 10.9,
 19.03,
 10.42,
 7.35,
 6.08,
 24.85,
 15.04,
 16.02,
 10.9,
 10.9,
 5.32,
 9.92,
 11.99,
 12.62,
 7.97,
 10.9,
 12.62,
 9.44,
 6.72,
 21.85,
 5.31,
 5.31,
 6.71,
 7.97,
 13.58,
 9.93,
 9.93,
 14.08,
 11.99,
 7.96,
 20.39,
 9.44,
 7.34,
 12.61,
 15.04,
 22.91,
 9.43,
 10.42,
 6.08,
 9.43,
 7.34,
 17.09,
 16.01,
 11.99,
 12.62,
 18.45,
 9.93,
 9.44,
 13.59,
 13.58,
 17.09,
 10.42,
 20.0,
 13.58,
 15.04,
 10.9,
 10.91,
 13.58,
 16.02,
 13.59,
 7.35,
 10.9,
 6.72,
 17.09,
 5.32,
 15.04,
 16.02,
 7.96,
 20.0,
 7.97,
 6.71,
 19.03,
 6.08,
 9.93,
 9.44,
 6.08,
 7.97,
 7.35,
 5.32,
 17.09,
 6.72,
 10.9,
 10.9,
 15.05,
 26.3,
 10.41,
 15.05,
 7.34,
 20.0,
 10.91,
 11.99,
 17.09,
 16.01,
 9.93,
 20.39,
 10.91,
 15.04,
 9.93,
 10.91,
 7.96,
 26.3,
 12.62,
 11.98,
 18.06,
 14.07,
 15.04,
 12.61,
 10.41,
 7.34,
 14.07,
 16.01,
 10.42,
 10.9,
 10.41,
 13.59,
 15.05,
 17.09,
 6.07,
 9.93,
 11.99,
 15.05,
 12.62,
 15.04,
 18.45,
 6.71,
 14.08,
 6.72,
 11.99,
 9.93,
 6.71,
 15.05,
 12.62,
 9.44,
 9.44,
 7.34,
 7.34,
 20.39,
 13.59,
 16.02,
 9.44,
 5.32,
 6.08,
 9.93,
 12.61,
 9.43,
 9.43,
 12.62,
 9.93,
 14.07,
 14.08,
 11.98,
 12.62,
 14.08,
 9.43,
 9.93,
 9.43,
 12.62,
 11.98,
 13.58,
 9.93,
 16.02,
 11.99,
 16.01,
 16.02,
 14.08,
 19.03,
 6.72,
 6.72,
 9.93,
 9.43,
 7.35,
 10.42,
 16.02,
 10.91,
 6.07,
 11.98,
 7.96,
 26.3,
 6.72,
 21.45,
 13.58,
 12.62,
 6.71,
 10.9,
 11.99,
 7.97,
 7.97,
 9.93,
 9.44,
 10.9,
 11.99,
 9.92,
 9.93,
 9.43,
 5.32,
 9.92,
 6.08,
 9.43,
 15.05,
 12.61,
 9.92,
 5.32,
 10.91,
 11.98,
 13.59,
 23.88,
 7.96,
 14.08,
 6.07,
 9.92,
 13.58,
 14.08,
 13.59,
 11.99,
 6.08,
 11.99,
 17.47,
 14.07,
 14.07,
 9.44,
 7.96,
 16.01,
 21.45,
 7.34,
 9.44,
 11.99,
 7.34,
 7.35,
 16.01,
 13.58,
 6.08,
 5.31,
 11.99,
 16.02,
 11.98,
 11.99,
 20.39,
 16.01,
 10.91,
 6.72,
 10.9,
 9.92,
 11.98,
 15.05,
 18.06,
 7.35,
 7.96,
 10.42,
 7.35,
 11.98,
 11.98,
 12.61,
 17.09,
 16.01,
 12.62,
 25.82,
 10.42,
 13.58,
 9.92,
 6.07,
 7.35,
 14.08,
 9.93,
 7.35,
 11.99,
 17.09,
 15.05,
 7.34,
 6.07,
 7.34,
 6.72,
 10.9,
 10.9,
 13.59,
 16.02,
 7.34,
 17.47,
 10.91,
 11.98,
 20.39,
 13.59,
 17.47,
 7.35,
 11.99,
 9.43,
 10.42,
 22.91,
 15.04,
 9.93,
 6.72,
 6.07,
 20.0,
 10.42,
 6.71,
 6.07,
 9.93,
 16.01,
 13.59,
 6.07,
 15.04,
 13.59,
 10.41,
 7.35,
 19.03,
 7.96,
 7.35,
 9.93,
 25.82,
 7.97,
 12.61,
 5.32,
 14.08,
 9.44,
 15.04,
 11.98,
 20.0,
 5.31,
 6.08,
 12.61,
 6.72,
 11.98,
 12.61,
 10.91,
 18.06,
 9.43,
 9.92,
 19.03,
 12.61,
 7.96,
 16.02,
 9.92,
 6.71,
 20.0,
 13.59,
 13.58,
 20.0,
 9.93,
 15.04,
 26.3,
 16.02,
 5.31,
 12.62,
 15.04,
 6.08,
 7.34,
 9.93,
 9.44,
 13.58,
 6.07,
 11.99,
 7.97,
 7.96,
 10.42,
 18.06,
 14.07,
 5.32,
 10.42,
 10.41,
 15.04,
 20.0,
 9.92,
 15.05,
 7.35,
 12.61,
 7.35,
 6.71,
 11.98,
 12.62,
 6.07,
 9.44,
 12.62,
 12.61,
 9.43,
 10.9,
 9.44,
 6.72,
 9.93,
 5.31,
 16.01,
 16.01,
 9.43,
 12.62,
 9.44,
 21.45,
 7.97,
 7.35,
 5.32,
 7.96,
 6.07,
 6.08,
 11.99,
 20.39,
 20.39,
 6.08,
 7.35,
 20.39,
 12.62,
 6.71,
 13.59,
 10.91,
 13.58,
 9.93,
 15.04,
 6.71,
 6.72,
 16.02,
 5.31,
 12.62,
 14.08,
 9.43,
 10.9,
 15.04,
 11.99,
 7.96,
 9.93,
 11.99,
 16.01,
 15.04,
 11.98,
 24.84,
 12.61,
 19.03,
 14.07,
 20.39,
 16.02,
 19.03,
 22.91,
 20.39,
 6.72,
 12.61,
 13.59,
 22.91,
 5.32,
 12.62,
 7.97,
 7.35,
 10.9,
 13.58,
 11.99,
 16.02,
 10.42,
 10.42,
 9.92,
 13.58,
 10.91,
 9.43,
 9.43,
 6.72,
 16.02,
 12.61,
 10.9,
 21.45,
 7.96,
 9.93,
 19.03,
 20.39,
 10.41,
 18.45,
 9.92,
 18.06,
 14.08,
 11.98,
 23.88,
 6.08,
 10.41,
 13.59,
 13.59,
 7.96,
 7.35,
 12.61,
 7.35,
 9.93,
 18.06,
 5.31,
 20.0,
 19.42,
 16.02,
 6.72,
 7.96,
 9.92,
 13.59,
 14.08,
 19.42,
 9.44,
 9.44,
 16.01,
 9.44,
 15.05,
 9.44,
 5.31,
 14.07,
 13.59,
 16.01,
 17.47,
 11.99,
 14.08,
 10.91,
 10.9,
 7.96,
 7.97,
 9.44,
 21.45,
 7.96,
 7.97,
 21.85,
 12.61,
 9.93,
 14.08,
 19.42,
 16.02,
 9.44,
 6.72,
 12.62,
 12.61,
 10.42,
 10.42,
 6.72,
 9.93,
 10.9,
 19.42,
 21.45,
 14.07,
 15.05,
 18.06,
 22.91,
 12.62,
 10.9,
 10.91,
 7.97,
 16.01,
 30.79,
 21.45,
 5.31,
 6.08,
 18.06,
 7.96,
 6.07,
 16.02,
 9.92,
 6.72,
 16.02,
 23.88,
 14.08,
 16.01,
 6.07,
 16.01,
 12.62,
 11.99,
 5.31,
 5.32,
 14.08,
 11.99,
 10.9,
 6.08,
 11.99,
 25.82,
 9.93,
 10.42,
 10.42,
 17.47,
 11.98,
 30.17,
 7.97,
 14.07,
 7.34,
 10.9,
 13.59,
 10.9,
 7.34,
 15.04,
 ...]


mean_squared_error(y_test,answer)

1.4872128000000004


plt.figure(figsize=(10,10))
plt.scatter(y_test, answer, c='crimson')


p1 = max(max(answer), max(y_test))
p2 = min(min(answer), min(y_test))
plt.plot([p1, p2], [p1, p2], 'b-')
plt.xlabel('True Values', fontsize=15)
plt.ylabel('Predictions', fontsize=15)
plt.axis('equal')
plt.show()

	emp_title	emp_length	state	homeownership	annual_income	verified_income	debt_to_income	annual_income_joint	verification_income_joint	debt_to_income_joint	...	sub_grade	issue_month	loan_status	initial_listing_status	disbursement_method	balance	paid_total	paid_principal	paid_interest	paid_late_fees
0	global config engineer	3.0	NJ	MORTGAGE	90000.0	Verified	18.01	NaN	NaN	NaN	...	C3	Mar-2018	Current	whole	Cash	27015.86	1999.33	984.14	1015.19	0.0
1	warehouse office clerk	10.0	HI	RENT	40000.0	Not Verified	5.04	NaN	NaN	NaN	...	C1	Feb-2018	Current	whole	Cash	4651.37	499.12	348.63	150.49	0.0
2	assembly	3.0	WI	RENT	40000.0	Source Verified	21.15	NaN	NaN	NaN	...	D1	Feb-2018	Current	fractional	Cash	1824.63	281.80	175.37	106.43	0.0
3	customer service	1.0	PA	RENT	30000.0	Not Verified	10.16	NaN	NaN	NaN	...	A3	Jan-2018	Current	whole	Cash	18853.26	3312.89	2746.74	566.15	0.0
4	security supervisor	10.0	CA	RENT	35000.0	Verified	57.96	57000.0	Verified	37.66	...	C3	Mar-2018	Current	whole	Cash	21430.15	2324.65	1569.85	754.80	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	owner	10.0	TX	RENT	108000.0	Source Verified	22.28	NaN	NaN	NaN	...	A4	Jan-2018	Current	whole	Cash	21586.34	2969.80	2413.66	556.14	0.0
9996	director	8.0	PA	MORTGAGE	121000.0	Verified	32.38	NaN	NaN	NaN	...	D3	Feb-2018	Current	whole	Cash	9147.44	1456.31	852.56	603.75	0.0
9997	toolmaker	10.0	CT	MORTGAGE	67000.0	Verified	45.26	107000.0	Source Verified	29.57	...	E2	Feb-2018	Current	fractional	Cash	27617.65	4620.80	2382.35	2238.45	0.0
9998	manager	1.0	WI	MORTGAGE	80000.0	Source Verified	11.99	NaN	NaN	NaN	...	A1	Feb-2018	Current	whole	Cash	21518.12	2873.31	2481.88	391.43	0.0
9999	operations analyst	3.0	CT	RENT	66000.0	Not Verified	20.82	NaN	NaN	NaN	...	B4	Feb-2018	Current	whole	Cash	11574.83	1658.56	1225.17	433.39	0.0

Description of dataset¶

Issues with the data set¶

3rd point: Creating a feature set and create a model which predicts interest_rate using at least 2 algorithms.¶

Propose enhancements to the model if given more time¶

Assumptions for this model¶

	emp_title	emp_length	state	homeownership	annual_income	verified_income	debt_to_income	annual_income_joint	verification_income_joint	debt_to_income_joint	...	grade	sub_grade	issue_month	initial_listing_status	disbursement_method	balance	paid_total	paid_principal	paid_interest	paid_late_fees
loan_status
Charged Off	6	6	7	7	7	7	7	1	1	1	...	7	7	7	7	7	7	7	7	7	7
Current	8585	8599	9375	9375	9375	9375	9354	1399	1362	1399	...	9375	9375	9375	9375	9375	9375	9375	9375	9375	9375
Fully Paid	419	420	447	447	447	447	445	59	57	59	...	447	447	447	447	447	447	447	447	447	447
In Grace Period	62	63	67	67	67	67	66	17	16	17	...	67	67	67	67	67	67	67	67	67	67
Late (16-30 days)	36	36	38	38	38	38	38	7	7	7	...	38	38	38	38	38	38	38	38	38	38
Late (31-120 days)	59	59	66	66	66	66	66	12	12	12	...	66	66	66	66	66	66	66	66	66	66

	loan_amount	term	grade	loan_purpose	A	B	C	D	E	F	G
0	28000	60	C	moving	0	0	1	0	0	0	0
1	5000	36	C	debt_consolidation	0	0	1	0	0	0	0
2	2000	36	D	other	0	0	0	1	0	0	0
3	21600	36	A	debt_consolidation	1	0	0	0	0	0	0
4	23000	36	C	credit_card	0	0	1	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...
9995	24000	36	A	other	1	0	0	0	0	0	0
9996	10000	36	D	debt_consolidation	0	0	0	1	0	0	0
9997	30000	36	E	debt_consolidation	0	0	0	0	1	0	0
9998	24000	36	A	other	1	0	0	0	0	0	0
9999	12800	36	B	credit_card	0	1	0	0	0	0	0

	loan_amount	term	A	B	C	D	E	F	G	car	...	debt_consolidation	home_improvement	house	major_purchase	medical	moving	other	renewable_energy	small_business	vacation
3157	10000	36	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	1	0	0	0
8260	7900	36	1	0	0	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	0
4717	5000	36	1	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	0
1087	21000	60	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	0
6293	12000	36	0	0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2881	4000	36	0	0	1	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	0
449	20000	60	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
6118	16000	60	0	0	1	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	0
5550	9000	36	0	1	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0
5463	25000	36	1	0	0	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	0

	interest_rate
3157	9.43
8260	6.71
4717	6.72
1087	26.77
6293	12.61
...	...
2881	15.05
449	7.96
6118	16.02
5550	9.43
5463	6.72